Fake News Detection Using Logistic Regression Method

Fake News is an immense issue that is increasing worldwide. Various rumors and false information are spreading all over the internet that increase false beliefs, biases and even violence among people. As a result, it is quite challenging to differentiate between true and fake in a world where people are entangled with various false and rumored news all around their mobile phones, news articles and social media platforms. To overcome this problem, various machine learning techniques have been used so far to find and detect the authenticity of news and information. In this study, the logistic regression technique has been used under supervised learning along with text preprocessing tools and feature extraction for classifying the news as either fake or real. It evaluates the output in 0s and 1s to find the authenticity of news either fake or real respectively. To ensure the performance, the high accuracy of the trained model has also been evaluated on accuracy metrics. This study helps to overcome the spread of misinformation over the internet.